Multifile probabilistic record linkage for drug overdose surveillance and public health action

用于药物过量监测和公共卫生行动的多文件概率记录链接

• 批准号: 10039949
• 负责人: Julia Elizabeth Hood
• 金额: $ 18.71万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10039949
• 关键词: Address; Adoption; Bayesian Method; Big Data Methods; Books; Characteristics; Complex; Computer software; County; Data; Data Collection; Data Sources; Databases; Date of birth; Death Certificates; Detection; Development; Emergency medical service; Ensure; Epidemiologist; Evaluation; Government; Government Agencies; Incidence; Individual; Information Systems; Internet; Interview; Jail; Light; Link; Measures; Methodology; Names; Output; Overdose; Performance; Pharmaceutical Preparations; Pilot Projects; Prevention program; Prevention strategy; Process; Public Health; Records; Techniques; Technology; Testing; Training; administrative database; base; data integration; flexibility; graphical user interface; improved; information model; multiple data sources; new technology; open source; operation; overdose prevention; overdose risk; referral services; simulation; treatment services; usability

Record linkage refers to the process of integrating data by identifying unique individuals within and across data sources. In administrative databases, it is common to have a limited amount of the individuals' partial identifiers, such as names or dates of birth, which together with typographical errors and missing data, makes the record linkage task difficult and prone to errors. Probabilistic record linkage approaches have been shown to have superior performance when compared with ruled-based deterministic techniques, as probabilistic approaches adapt better to different and increased levels of error in the datafiles. Existing probabilistic approaches are nevertheless subject to different limitations. In practice, it is common to encounter data integration scenarios where multiple data sources need to be simultaneously merged and deduplicated using imperfect information such as names, dates or addresses. These scenarios go beyond the specifications for which commonly used record linkage and deduplication methodologies have been developed. We therefore propose to extend the currently-available best-performing record linkage methodologies to simultaneously integrate multiple datafiles and detect duplicated records within them. We will develop this methodology, with an associated software and graphical user interface, in partnership with Public Health – Seattle & King County to ensure that these are responsive to real world needs and challenges. We will also conduct a pilot study implementing the techniques on King County administrative data systems used for overdose surveillance and evaluation of overdose prevention programs.

记录链接是指通过识别数据内和数据间的唯一个体来集成数据的过程 来源。在行政数据库中，个人的部分信息数量有限是很常见的。 标识符，例如姓名或出生日期，再加上印刷错误和缺失数据，使得 记录联动任务艰巨且容易出错。已经展示了概率记录链接方法 与基于规则的确定性技术相比，具有优越的性能，如概率 方法更好地适应数据文件中不同的和增加的错误级别。现有概率 然而，各种方法都受到不同的限制。在实际工作中，经常会遇到数据 需要同时合并多个数据源并进行重复数据删除的集成场景 不完整的信息，例如姓名、日期或地址。这些场景超出了规范 已开发出常用的记录链接和重复数据删除方法。我们因此 建议将目前可用的性能最佳的记录链接方法扩展到同时 集成多个数据文件并检测其中的重复记录。我们将开发这种方法， 与西雅图和金县公共卫生部门合作的相关软件和图形用户界面 确保这些能够响应现实世界的需求和挑战。我们还将进行试点研究 在金县行政数据系统上实施用于过量监测的技术 评估过量预防计划。